As used herein, the term “industrial process facility” shall mean any facility that is adapted to refine, purify, convert, consume, or otherwise process any material to produce desired products, and includes, but is not limited to, petroleum refineries, catalytic and steam crackers, petrochemical plants, other chemical plants (i.e., chemical plants that are not based on petrochemicals), facilities for converting natural gas and/or methanol into other hydrocarbons, natural gas liquefaction plants, polymer and plastics plants, steel mills, pharmaceutical manufacturing plants, and electric power generating plants. In some cases, an industrial process facility may include two or more separate facilities, such as a petroleum refinery together with one or more steam crackers.
Industrial process facilities typically consist of a plurality of separate process units or sections of process units that function together to achieve the overall objective of the facility. As used herein, a “process unit” is an apparatus or the equipment that performs a specific function, such as a process gas compressor unit, a fractionator, a reformer, a hydrotreater, a distillation column, a quench tower, a de-ethanizer, a cogeneration unit, or a refrigeration unit, and a “section” of process units is a grouping of two or more associated process units, such as the recovery section of a steam cracker.
Difficulties arise in effectively controlling and optimizing the operation of an industrial process facility due to factors such as the wide variety of separate process units and equipment that may be contained in the facility, the large number of process variables, the large number of potential feedstocks and feedstock compositions, operating variables (e.g., flow rates, temperatures, pressures, etc.), product specifications, market constraints and prices (e.g., for feeds, products, and utilities), mechanical constraints, transportation and storage constraints, and weather conditions. Consequently, the industrial process industries have attempted to develop computer models that can be used to accurately simulate and/or optimize the operation of an industrial process facility.
Commercially available tools have been developed to facilitate such computer modeling and optimization. These commercially available tools can be broken down into two distinct types: first principles reference tools and derived tools.
First principles reference tools are tools that are based on first principles (i.e., mathematical relationships or logic that utilize accepted scientific theories or laws, such as those regarding chemical thermodynamics and/or kinetics, which theories or laws have been validated through repeated experimental tests) and that typically possess the capability to separately model many or all of the individual process units in an industrial process facility. First principles reference tools also typically possess a library that provides thermodynamic information about how different molecules, components, or pseudo-components will perform in these process units. These tools can be used to create a model of an industrial process facility, or section thereof, by using the thermodynamic library to individually model the various process units in the facility and then connecting the process units appropriately to reflect the overall facility. Such a model can then directly provide heat and material balance information, which can be used for design, equipment rating, equipment performance, simulation, and optimization of the facility. Unfortunately, first principles reference models tend to be computationally intensive and, accordingly, substantial computer time and resources can be required to run a model based thereon. Examples of commercially available first principles reference tools are HYSIS® and Aspen Plus®, which are products of Aspen Technologies Incorporated of Cambridge, Mass.; PRO/II®, which is a product of SimSci-Esscor, an operating unit of Invensys plc of Cheshire, United Kingdom; and SPYRO®, which is a product of Technip-Coflexip SA of Paris, France.
Derived tools are tools that possess very convenient structures to depict many or all of the process unit operations needed to model an industrial process facility. These derived tools have convenient report writing capabilities, and may possess various analysis tools to help explain the modeling results. In general, derived tools use either linear programming (LP) or sequential linear programming (SLP) type mathematics to solve optimization problems. However, these tools do not have the capability to model process unit operations based on first principles, nor do they contain a thermodynamic library to describe how different molecules, components, or pseudo-components would perform in such process unit operations. As such, these derived tools cannot directly provide heat and material balance information for use in design, equipment rating, equipment performance, simulation, and optimization of the facility. Rather, to create a model in these derived tools requires that a depiction of the facility to be modeled be developed in some other engineering tool (e.g., HYSIS®, Aspen Plus®, PRO/II®, and SPYRO®, referred to above, as well as other commercially available engineering tools that would be well known to persons skilled in the art of modeling industrial process facilities), and this depiction is then imported into the derived tool. Nevertheless, given their convenient form and analysis capabilities, as well as the computing advantages of LP or SLP programming, derived tools have generally been preferred for use in operational planning, feedstock selection, and optimization of an industrial process facility. Examples of commercially available derived tools are AspenTech PIMS®, which is a product of Aspen Technology Incorporated of Cambridge, Mass., and SimSci Petro®, which is a product of SimSci-Esscor, an operating unit of Invensys plc, of Cheshire, United Kingdom.
Heretofore, creating and maintaining a derived computer model of an industrial process facility has, of necessity, been a piecemeal process. Separate models of individual process units in the facility had to be created and then interconnected to represent the overall facility, and intermediate stream connectivities had to be accounted for. An intermediate stream is a stream that flows from one process unit into one or more other process units. For example, a product stream from an upstream process unit may become an input stream to one or more downstream process units, or a recycle stream from a downstream process unit may become an input stream to one or more upstream process units. Thus, a change in the products from a particular upstream process unit may cause a change in a recycle stream from a downstream process unit, which in turn may cause another change in the same or a different upstream process unit. The overall derived computer model for the facility must accurately model these effects.
As an example, the following steps have typically been required to create a derived model of a steam cracker:                First, a suitable depiction of each of the process units in the steam cracker for which a model is desired must be identified and/or developed. As noted above, such a depiction has typically come from some form of separate engineering tool. Often, the engineering tools used differ depending on the process unit being modeled, and the use of different engineering tools to create the process unit depictions can result in consistency problems within the final derived model. Sometimes, only a simple mass balance depiction may be used, but such a simple depiction may miss important energy effects, and may not be able to represent constraints with sufficient accuracy. Suitable constraint depiction and energy representation usually requires heat and mass balance information, which in turn often comes from a first principles reference model including some underlying thermodynamic properties representation.        Second, after the depiction of an individual process unit has been developed, it is “perturbed,” usually by altering a single independent input variable (often referred to as a “x-variable,” such as a single component feed rate or operating parameter) with the important output variables (often referred to as “y-variables”) being monitored. In a well-defined derived model for a given process unit of a steam cracker, the number of x-variables would include all independent variables, which in turn would translate to one for each component in each stream entering the process unit, plus the number of independent operating conditions in the process unit that can be perturbed, plus the number of tuning variables available in the process unit. Important output variables are those that directly impact economic performance of the steam cracker (such as product rates or qualities, energy consumption, and process constraints), as well as other key performance variables that are considered important to monitor within the steam cracker.        Then, what is known as a “vector” or “shift” is created. Such a vector or shift is simply the partial derivative of each of the y-variables with respect to the perturbed x-variable. This “partial derivative” may be either an “analytical” partial derivative or a “numerical” partial derivative.        Next, the perturbed x-variable is reset to its original value and the next x-variable is perturbed, with a new vector or shift being calculated for this x-variable. This process is repeated until all of the independent x-variables have been perturbed. The result of this process is a matrix containing the partial derivatives of each y-variable with respect to each x-variable for the particular process unit in question.        Finally, the matrices for each of the individual process units are interconnected (with intermediate stream connectivities accounted for) so as to create a derived model of the overall facility.        
There are several problems inherent in the procedure described above. For example, a complex steam cracker may have 15 or more separate process units that must be accurately modeled to create the overall derived model. It is unlikely that depictions for all of these process units can be developed in the same engineering tool, let alone on a totally consistent heat and material balance basis. Moreover, complex steam crackers typically have a large number of recycle streams that must be accurately modeled. When the individual derived models for each process unit and recycle streams are joined together to form the overall derived model for the facility, inconsistencies between individual derived models (e.g., inconsistencies in the underlying engineering tools or in the heat and material balance basis) can result in a more difficult validation process and, in some situations, in non-convergence or unacceptable inaccuracies in the overall model.
Obviously, the above approach to model development is very difficult. But the Achilles Heel of this approach has been in the maintenance of the derived model over time to ensure that the model continues to accurately reflect to the steam cracker. In actuality, steam cracker performance changes over time. For example, process units become fouled over time (or get cleaned). Operating configurations are altered (e.g., multiple processing options exist, and these options change from time to time), and often new processing options are introduced. Further, the steam cracker recovery section performance (particularly in compressor areas) is actually not linear at all. While this performance can be adequately approximated by a linear relationship over a portion of the operating window, major moves from the original derivation point often result in significant non-linear effects that cause the original depiction and derivatives based thereon to become too inaccurate to adequately represent the steam cracker. Hence, frequent re-derivation of the derivatives may be required to ensure that the model continues to accurately represent the steam cracker.
The process industries have attempted to resolve these problems. However, to date, none of the proposed solutions has been entirely satisfactory.
U.S. Patent Application Publication No. 2003/0097243 A1 discloses a computerized system and method for operating a hydrocarbon or chemical production facility, comprising mathematically modeling the facility; optimizing the mathematic model with a combination of linear and non-linear solvers; and generating one or more product recipes based upon the optimized solution. In one embodiment, the mathematic model further comprises a plurality of process equations having process variables and corresponding coefficients. Preferably, these process variables and corresponding coefficients are used to create a matrix in a linear program. The linear program may be executed by recursion or distributed recursion. Upon successive recursion passes, updated values for a portion of the process variables and corresponding coefficients are calculated by the linear solver and by a non-linear solver, and the updated values for the process variables and corresponding coefficients are substituted into the matrix. Unfortunately, the simultaneous use of multiple solvers, some of which are non-linear, can result in significant computing time and resource disadvantages.
U.S. Pat. No. 5,666,297 discloses a software system for simulating and optimizing a processing plant design. The software system includes a plurality of dual mode equipment models for simulating each piece of equipment in the processing plant design. A sequential modular simulation routine is used to execute the equipment models in a first mode to define a first set of values of the operating parameters of the processing plant design. Then, a simultaneous simulation/optimization routine executes the equipment models in a second mode. The simultaneous simulation/optimization routine utilizes the first set of values for the plant's operating parameters from the sequential simulation routine and subsequently determines a second set of values of the operating parameters at which the processing plant design is optimized. The equipment models after execution by the sequential simulation routine and the simultaneous simulation/optimization routine store the first and second sets of values for the operating parameters in a common plant model file.
U.S. Pat. No. 6,442,513 discloses a method for real-time optimization of an oil refinery, or a portion thereof, wherein a fluid stream having multiple physical components is modeled as a plurality of pseudo-components. Each physical component has a boiling point, and each pseudo-component has a pre-defined boiling point and includes all physical components from the fluid stream having approximately the pre-defined boiling point. According to this patent, good modeling results may be obtained by grouping compounds and molecules into pseudo-components or lumps based on boiling points, and by modeling based on such lumps. This is especially true in view of the fact that much of the operation of a refinery depends on boiling points of compositional components of crude oil.
U.S. Patent Application Publication No. 2003/0097194 A1 discloses a method for pre-calculating the parameters of industrial processes and/or products. According to this method, a vector of admissible input variables of the industrial process and/or product is defined. Definition ranges are assigned to each variable in the input vector. A process output vector is determined with the pre-calculable process parameters. Known information on the process is stored in a data bank and ranges of validity for the process input variables are allocated to this information. For each process input vector inputted from an admissible definition range provided with valid information, exactly one process output vector is determined according to the information valid therefor.
Recently, a new generation of first principles reference tools has been developed that are capable of modeling, solving, and optimizing an entire industrial process facility. Examples of these new reference tools are AspenTech RT-OPT®, which is a product of Aspen Technology Incorporated of Cambridge, Mass., and SimSci ROMeo®, which is a product of SimSci-Esscor, an operating unit of Invensys plc, of Cheshire, United Kingdom. These tools are capable of solving very large optimization problems, usually via a non-linear simultaneous equation solver and optimizer. However, given the enormous size and complexity of a first principles reference model for an entire industrial process facility, as well as its non-linear nature, solution of the model can require huge amounts of computing resources and can take substantial periods of time, especially in optimization mode.
Obviously, there is a need for a method to combine the modeling capabilities of this new generation of first principles reference tools with the computing advantages of linear programming (LP) or sequential linear programming (SLP) models. The present invention satisfies this need.